Advancements in the field of digital imaging have led to a rapid development of various image processing and computer-vision algorithms to solve classical problems, such as texture synthesis, image filtering and enhancement, image reconstruction, image segmentation, 3D stereo matching and/or motion estimation. Identifying correspondences between image patches by a patch-matching technique may be a core operation in many of these image processing and computer-vision algorithms. Typically, given a target patch in an image, the task is to find one or more neighbor patches which are similar to the target patch. The search scope may be restricted to the same image that contains the target patch, multiple images, or some neighboring regions within such images.
In certain scenarios, the patch matching technique for single channel images may be relatively simple. In such a case, a similarity between two patches may be measured by an appropriate matching cost function based on pixel-wise comparison of the two patches. However, currently, a lot of images captured are multi-channel images. Examples include digital color photography whereby the captured scenes are represented in the R, G, B channels, and multispectral imaging which records image data at multiple electromagnetic frequencies. The existing techniques for patch matching may not utilize channel information from all the channels, which may lead to suboptimal processing results.
Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.